Fourier descriptors (FFT) and Hu's seven moment invariants (HSMI) are among the most popular shape-based image descriptors and have been used in various applications, such as recognition, indexing, and retrieval. In this work, we propose to use the invariance properties of Hu's seven moment invariants, as shape feature descriptors, for relevance identification in content-based image retrieval (CBIR) systems. The purpose of relevance identification is to find a collection of images that are statistically similar to, or match with, an original query image from within a large visual database. An automatic relevance identification module in the search engine is structured around an unsupervised learning algorithm, the self-organizing tree map (SOTM). In this paper we also proposed a new ranking function in the structure of the SOTM that exponentially ranks the retrieved images based on their similarities with respect to the query image. Furthermore, we propose to extend our studies to optimize the contribution of individual feature descriptors for enhancing the retrieval results. The proposed CBIR system is compatible with the different architectures of other CBIR systems in terms of its ability to adapt to different similarity matching algorithms for relevance identification purposes, whilst offering flexibility of choice for alternative optimization and weight estimation techniques. Experimental results demonstrate the satisfactory performance of the proposed CBIR system.